Forecasting daily PM2.5 concentrations in Wuhan with a spatial-autocorrelation-based long short-term memory model-Reference-Cited by-同舟云学术

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Forecasting daily PM2.5 concentrations in Wuhan with a spatial-autocorrelation-based long short-term memory model

Published:2024-08 Issue: Volume:331 Page:120605
ISSN:1352-2310
Container-title:Atmospheric Environment
language:en
Short-container-title:Atmospheric Environment

Author:

Liu Zhifei^ORCID,Ge Chengjun^ORCID,Zheng Kang,Bao Shuai^ORCID,Cui Yide,Yuan Yirong,Zhang Yixuan

Publisher

Elsevier BV

Reference75 articles.

1. LSTM inefficiency in long-term dependencies regression problems;Al-Selwi;J. Adv. Res. Appl. Sci. Eng. Technol.,2023

2. Ambient PM2. 5 exposure and mortality due to lung cancer and cardiopulmonary diseases in Polish cities;Badyda;Respir. Treat. Prev.,2017

3. PM2. 5 concentration prediction using deep learning in internet of things air monitoring system;Bai;Environ. Eng. Res.,2023

4. Enviro-HIRLAM online integrated meteorology–chemistry modelling system: strategy, methodology, developments and applications (v7.2);Baklanov;Geosci. Model Dev. (GMD),2017

5. Air-pollution prediction in smart city, deep learning approach;Bekkar;J. Big Data,2021

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1. Daily PM2.5 concentration prediction based on variational modal decomposition and deep learning for multi-site temporal and spatial fusion of meteorological factors;Environmental Monitoring and Assessment;2024-08-29

2. Utilizing Deep Learning and Spatial Analysis for Accurate Forest Fire Occurrence Forecasting in the Central Region of China;Forests;2024-08-07

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